Driving Simulators for Occupational Therapy Screening, Assessment, and Intervention
Bibliographic record
Abstract
Simulation technology provides safe, objective, and repeatable performance measures pertaining to operational (e.g., avoiding a collision) or tactical (e.g., lane maintenance) driver behaviors. Many occupational therapy researchers and others are using driving simulators to test a variety of applications across diverse populations. A growing body of literature provides support for associations between simulated driving and actual on-road driving. One limitation of simulator technology is the occurrence of simulator sickness, but management strategies exist to curtail or mitigate its onset. Based on the literature review and a consensus process, five consensus statements are presented to support the use of driving simulation technology among occupational therapy practitioners. The evidence suggests that by using driving simulators occupational therapy practitioners may detect underlying impairments in driving performance, identify driving errors in at-risk drivers; differentiate between driving performance of impaired and healthy controls groups; show driving errors with absolute and relative validity compared to on-road studies; and mitigate the onset of simulator sickness. Much progress has been made among occupational therapy researchers and practitioners in the use of driving simulation technology; however, empirical support is needed to further justify the use of driving simulators in clinical practice settings as a valid, reliable, clinical useful, and cost effective tool for driving assessment and intervention.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".